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29th International Conference on database Systems for Advanced Applications (DASFAA)

作者： Liu, Jingyang Wang, Nan Zhong, Yingli Shen, Zhonghui Heilongjiang Univ Coll Comp Sci & Technol Harbin 150080 Peoples R China Heilongjiang Univ Key Lab Database & Parallel Comp Harbin 150080 Peoples R China Heilongjiang Univ Sci & Technol Dept Harbin Peoples R China

ISBN: (纸本)9789819757787;9789819757794

Sequence recommendation systems usually learn users personalized preferences based on historical behavior sequences of users. Previous methods often use rich item interaction informations combined with context to mine user sequential patterns. However, the time decay and dynamic evolution of users preferences are rarely taken into consideration. In this paper, we propose TDH4Rec(Time-aware Dual-kernel Hawkes process for sequential recommendation). First, we establish a time-aware attention embedding module. We utilize time sensitivity and importance of interactions to capture the time dependence and frequency dependence of items during the interaction process. Secondly, we design a Hawkes process based dual-kernel learning module. The Hawkes interaction kernel function with historical interaction effect and the Hawkes time kernel function with time decay effect are designed to capture the influence of historical interaction and the influence of time change of interacted items, respectively. Finally, extensive experiments on three real-world datasets show the efficacy of our model compared with conventional methods and state-of-the-art methods.

关键词： Hawkes Process Sequential Recommendation Self Attention

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Knowledge-driven hierarchical intents modeling for recommendation

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Zeng, Jin Wang, Nan Li, Jinbao Heilongjiang Univ Coll Comp Sci & Technol Harbin 150080 Peoples R China Heilongjiang Univ Key Lab Database & Parallel Comp Harbin 150080 Peoples R China Qilu Univ Technol Shandong Artificial Intelligence Inst Jinan 250353 Peoples R China Qilu Univ Technol Sch Math & Stat Jinan 250353 Peoples R China

Previous studies on user-item interaction graphs have typically concentrated on simple interactions, often overlooking the significant role of user intent in shaping these interactions. While some recent research has explored intent relationships to enhance modeling, these approaches mainly focus on user preferences derived from interactions, ignoring the knowledge information with good interpretation in knowledge graphs. In addition, some recent work usually utilize undenoised graph structure information to learn the node representations, which introduces plenty of noise and impedes the well learning of users' preference. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel knowledge-driven hierarchical intent modeling framework called KHIM. The focus is on designing a hierarchical user intent modeling process and an intent-based multi-view contrastive learning mechanism. The former extracts both the popular and personalized preferences of users from attribute tuples within the knowledge graph at the global-level and local-level, respectively. For global level, we capture the user's true intents from positive view and augment the positive intent representation with negative view. While the latter generates high-quality user and item representations through multi-level cross-view contrastive learning. Two data augmentation strategies are designed during the contrastive process to mitigate the effects of noise in the learning process. Additionally, we also designed a neighbor filtering strategy based on semantic view to obtain more neighbor semantic information of user and item nodes, so as to further improve the recommendation performance. Experimental results on three benchmark datasets demonstrate that KHIM significantly outperforms various state-of-the-art approaches, highlighting its effectiveness in leveraging knowledge graph information for better recommendations.

关键词： Recommendation Knowledge graph Contrastive learning

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Quick-MergeSort Algorithm Based on Multi-coreLinux

Quick-MergeSort Algorithm Based on Multi-coreLinux

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International Conference on Mechatronic Sciences, Electric Engineering and computer (MEC)

作者： Liu, Yang Yang, Yan Heilongjiang Univ Sch Comp Sci & Technol Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (纸本)9781479925650

Sorting a list of input numbers is one of the most fundamental problems in the field of computer science. This paper presents an efficient implementation and detailed analysis of quick sort algorithm called QM-sort on Multi-core CPU architectures. Our QM-sort has two stages. The first stage is making blocks sorted, and the second stage is merging the sorted blocks. At the first stage, we propose a parallel quick sort algorithm named BPQsort. The run time of BPQsort achieves 40%-50% better than Qsort (STL), so we make blocks sorted more efficient. On the other hand, our algorithm can read file in parallel, thus reduce the time of reading file from disk. At the second stage, our parallel merge algorithm can operate the data on consecutive memory. Thus we avoid the expensive unaligned load/store operations, thus make our merge algorithm faster. At last, the run time of QM-sort is 10%-15% better than quick sort based on OpenMP.

关键词： QuickSort Sort Multi-core CPU Linux Multi-thread

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Data caching-based query processing in multi-sink wireless sensor networks

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2012年第2期11卷 109-125页

作者： Cai, Zhipeng Ji, Shouling Li, Jinbao Heilongjiang Univ Sch Comp Sci & Technol Key Lab Database & Parallel Comp Heilongjiang Pro Harbin 150080 Heilongjiang Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

In multi-sink Wireless Sensor Networks (WSNs), different sinks may issue same queries and therefore they can share some common query results. As a classical data-sharing method, data caching is employed widely in traditional databases and it can also bring benefits in WSNs thanks to the emergence of sensors with Flash Memory. Therefore, a query-processing method based on common subtree (CS) caching in multi-sink WSNs is proposed. We first construct CSs and then cache the results of processed queries at the roots of CSs. Subsequently, later common queries can obtain their expected results from the common roots. Furthermore, in order to achieve a higher degree of data sharing, we propose a loop-removing algorithm. Extensive simulations indicate that the proposed method can reduce the energy consumption and the query response time significantly for WSNs.

关键词： WSNs wireless sensor networks data management query processing data caching multi-sink data sharing Flash Memory

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MFIP: Multi-Factor Interlinked Point-of-Interest Recommendation in Location-Based Social Network

MFIP: Multi-Factor Interlinked Point-of-Interest Recommendat...

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IEEE International Performance, computing, and Communications Conference (IPCCC)

作者： Lu, Qiaojie Wang, Nan Li, Kun Heilongjiang Univ Sch Comp Sci & Technol Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Heilongjiang Peoples R China

ISBN: (数字)9781665480185

ISBN: (纸本)9781665480185

The paper proposes a multi-factor interlinked POI recommendation model called MFIP. Extracting user similarity for user-sensitive implicit modeling to enrich user representation. Using contextual information such as sequential, geographical and social to construct a POI recommendation model with collaborative influence o f multi-factor, a lleviating data sparsity. A novel multi-factor interlinked strategy(FIS) is proposed that can dynamically adjust the user preferences of different factors to obtain the comprehensive impact of user personalization. In addition, we propose a active area selection algorithm based on segmentation to model the geographical information more effectively. Finally, we conduct a comprehensive performance evaluation for MFIP on two large-scale real world check-in datasets collected from Gowalla and Yelp. Experimental results show that MFIP achieves significantly superior recommendation performance compared to other state-of-the-art POI recommendation models.

关键词： POI recommendation multi-factor implicit feedback interlinked strategy

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ShuffleVITNet: MOBILE-FRIENDLY VISION TRANSFORMER With LESS-MEMORY

ShuffleVITNet: MOBILE-FRIENDLY VISION TRANSFORMER With LESS-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhao, Xinyu Lu, Jun Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Traditional Convolutional Neural Networks (CNNs) can efficiently acquire local features, while ViT uses a Transformer structure that captures global contextual information. In this paper, a new high-performance lightweight target detector is designed by combining the respective advantages of CNN and ViT: ShuffleViTNet. In this paper, we improve the VIT Block in MobileVIT, firstly, we use channel-by-channel convolution to replace the normal convolution to reduce the memory access cost (MAC), number of parameters (Parmas) and floating-point operations (FLOPs) in the model inference. The 1x1 convolution is then used to fuse the local features learned by the convolution with the global features learned by the Transofmer, which further lightens the model and allows for easier modification of the number of channels and migration to other lightweight neural networks. Finally a lightweight network friendly to mobile devices is constructed using the improved VIT Block. On the ImageNet1k dataset, ShuffleViTNet achieves 75.2% Top-1 accuracy, the model's floating-point operation is 536.5 MFLOPs, which is 44.0% less compared to MobileViT-XS, and the cost of memory accesses for model inference is saved by about 25.6%, and the required memory is saved by about 66.37%. For the object detection task on the MS-COCO dataset, ShuffleViTNet obtains higher mAP accuracy. The experimental results show that ShuffleViTNet is a high-performance lightweight network and can be well applied to downstream tasks.

关键词： light-weight network CNN-Transformer hybrid architecture image classification object detection

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A Simplified Bert Filter Denoising Model for Sequence Recommendation 24

A Simplified Bert Filter Denoising Model for Sequence Recomm...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Xie, Yunyang Wang, Nan Xu, Xin Zhong, Yingli Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Heilongjiang Univ Key Lab Database & Parallel Comp Harbin Peoples R China

ISBN: (纸本)9798400718144

In recent years, some deep neural network models like Recurrent Neural Network (RNN), transformer and Bert have been applied to sequence recommendation. It aims to obtain dynamic preference features from recorded user behavior data, so as to achieve accurate recommendations. However, the user's interaction history inevitably contains a lot of noise information, which is easy to mislead the training of the model. In order to address this issue, we propose a novel simplified Bert filter denoising sequence recommendation model (LightBertR). LightBertR adds a filter layer Bertf with a learnable filter to Bert, which adaptively attenuates noise information by converting time domain and frequency domain information to each other. The incorporation of the Bert filter layer facilitates the model's capacity to discern the intrinsic characteristics of the denoised interaction sequence, thereby enhancing the efficacy of the sequence recommendation model. Furthermore, LightBertR reduces the number of transformer layers in Bert model, markedly diminishes the training parameters of the model, and expedites model training. Extensive experiments on two real datasets demonstrate that LightBertR outperforms other Bert-based methods and numerous baseline methods in the recommendation field.

关键词： transformer Bert denoising Filter frequency domain time domain

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Rogue Access Point Detection in Vehicular Environments 10th

Rogue Access Point Detection in Vehicular Environments

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10th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Qu, Hao Guo, Longjiang Zhang, Weiping Li, Jinbao Ren, Meirui Heilongjiang Univ Sch Comp Sci & Technol Harbin 150080 Peoples R China Key Lab Database & Parallel Comp Harbin 150080 Peoples R China

ISBN: (纸本)9783319218373;9783319218366

The threat of rogue access points (APs) has attracted significant attentions from both industrial and academic researchers. This paper considers a category of rogue APs that are set up in moving vehicles to lure users. Usually, rogue access points are on the moving vehicles that can keep close distance to the users at all time, thus the adversary has more time to expose private information of users. This paper proposes a practical detecting rogue APs algorithm based on received signal strength (RSS), which calculates the distance between the user and the APs to defend against the rogue APs. The paper also develops a relative position algorithm to find the position of AP. If the position of AP is on the road rather than on the road side, the algorithm suspects that the AP is rogue AP. In this paper, we also have implemented the detection algorithm on real vehicular environments and also have evaluated the performance of the proposed algorithm.

关键词： Rogue access point Vehicular networks RSS

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Research on Jukes-Cantor Model parallel Algorithm Based on OpenMP 1

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1st National Conference on Big Data Technology and Applications (BDTA)

作者： Wan, Kang Lu, Jun Zheng, Jiaju Ren, Zihan Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (数字)9789811004575

ISBN: (纸本)9789811004575;9789811004568

The parallel algorithm of the evolutionary distance between different species is implemented by using OpenMP parallel technique in this paper. In order to get the best degree of algorithm parallelism, the method of making loop variable corresponding to the rower and column labels of distance matrix is adopted. It is to say that the double loop can be converted into single loop to improve the parallel efficiency. The serial algorithm and parallel algorithm are compared in this paper. The experiment result shows that the highest speedup is 14.1. It improves the running efficiency of the program. It is a great significance to dealing with massive bioinformatics data.

关键词： OpenMP parallel Evolutionary distance Distance matrix

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A Spatial Correlation Based Adaptive Missing Data Estimation Algorithm in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2014年第4期21卷 280-289页

作者： Pan, Liqiang Gao, Huijun Gao, Hong Liu, Yong Harbin Inst Technol 92 West Da Zhi St Harbin Peoples R China Heilongjiang Univ Key Lab Database Parallel Comp Heilongjiang Prov Harbin Peoples R China

In wireless sensor networks, the missing of sensor data is inevitable due to the inherent characteristic of wireless sensor networks, and it causes many difficulties in various applications. To solve the problem, the missing data should be estimated as accurately as possible. In this paper, an adaptive missing data estimation algorithm is proposed based on the spatial correlation of sensor data. It adopts multiple regression model to estimate the missing data with the data of multiple neighbor nodes jointly rather than independently, which makes its estimation performance stable and reliable. In addition, for different missing data, it can adjust the estimation equation adaptively to capture the dynamic correlation of sensor data. Thereby, it can estimate the missing data more accurately. Further more, it can also give the confidence interval of each missing data for the given confidence level, which is helpful greatly for users. Experimental results on two real-world datasets show that the proposed algorithm can estimate the missing data accurately.

关键词： Missing data Estimation Spatial correlation Wireless sensor networks

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